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  <section id="module-causalai.models.common.CI_tests">
<span id="kci-test-module"></span><h1>KCI Test module<a class="headerlink" href="#module-causalai.models.common.CI_tests" title="Permalink to this heading"></a></h1>
<section id="causalai-models-common-ci-tests-kci">
<h2>causalai.models.common.CI_tests.kci<a class="headerlink" href="#causalai-models-common-ci-tests-kci" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="causalai.models.common.CI_tests.kci.KCI">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">causalai.models.common.CI_tests.kci.</span></span><span class="sig-name descname"><span class="pre">KCI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Xkernel:</span> <span class="pre">~causalai.models.common.CI_tests.kernels.KernelBase</span> <span class="pre">=</span> <span class="pre">&lt;causalai.models.common.CI_tests.kernels.GaussianKernel</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Ykernel:</span> <span class="pre">~causalai.models.common.CI_tests.kernels.KernelBase</span> <span class="pre">=</span> <span class="pre">&lt;causalai.models.common.CI_tests.kernels.GaussianKernel</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Zkernel:</span> <span class="pre">~causalai.models.common.CI_tests.kernels.KernelBase</span> <span class="pre">=</span> <span class="pre">&lt;causalai.models.common.CI_tests.kernels.GaussianKernel</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">null_space_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">5000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">approx:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#causalai.models.common.CI_tests.kci.KCI" title="Permalink to this definition"></a></dt>
<dd><p>Kernel-based Conditional Independence (KCI) test.
Original implementation: <a class="reference external" href="http://people.tuebingen.mpg.de/kzhang/KCI-test.zip">http://people.tuebingen.mpg.de/kzhang/KCI-test.zip</a></p>
<section id="references">
<h3>References<a class="headerlink" href="#references" title="Permalink to this heading"></a></h3>
<p>[1] K. Zhang, J. Peters, D. Janzing, and B. Schölkopf, &quot;A kernel-based conditional independence test and application in causal discovery,&quot; In UAI 2011.</p>
<dl class="py method">
<dt class="sig sig-object py" id="causalai.models.common.CI_tests.kci.KCI.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Xkernel:</span> <span class="pre">~causalai.models.common.CI_tests.kernels.KernelBase</span> <span class="pre">=</span> <span class="pre">&lt;causalai.models.common.CI_tests.kernels.GaussianKernel</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Ykernel:</span> <span class="pre">~causalai.models.common.CI_tests.kernels.KernelBase</span> <span class="pre">=</span> <span class="pre">&lt;causalai.models.common.CI_tests.kernels.GaussianKernel</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Zkernel:</span> <span class="pre">~causalai.models.common.CI_tests.kernels.KernelBase</span> <span class="pre">=</span> <span class="pre">&lt;causalai.models.common.CI_tests.kernels.GaussianKernel</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">null_space_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">5000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">approx:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#causalai.models.common.CI_tests.kci.KCI.__init__" title="Permalink to this definition"></a></dt>
<dd><p>KCI test constructor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>Xkernel</strong> (<em>KernelBase object</em>) -- kernel class instance for input data x. Available options are GaussianKernel and LinearKernel.</p></li>
<li><p><strong>Ykernel</strong> (<em>KernelBase object</em>) -- kernel class instance for input data y. Available options are GaussianKernel and LinearKernel.</p></li>
<li><p><strong>Zkernel</strong> (<em>KernelBase object</em>) -- kernel class instance for input data z (conditional variables). Available options are GaussianKernel and LinearKernel.</p></li>
<li><p><strong>null_space_size</strong> (<em>int</em>) -- sample size in simulating the null distribution (default=5000).</p></li>
<li><p><strong>approx</strong> (<em>bool</em>) -- whether to use gamma approximation (default=True).</p></li>
<li><p><strong>chunk_size</strong> (<em>int</em>) -- if number of data samples is more than chunk_size (default=1000), only extract the block-wise diagonal kernel matrix
of the full kernel matrix to save memory and computation.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="causalai.models.common.CI_tests.kci.KCI.run_test">
<span class="sig-name descname"><span class="pre">run_test</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_z</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#causalai.models.common.CI_tests.kci.KCI.run_test" title="Permalink to this definition"></a></dt>
<dd><p>compute the test statistics and pvalues</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data_x</strong> (<em>ndarray</em>) -- input data for x</p></li>
<li><p><strong>data_y</strong> (<em>ndarray</em>) -- input data for y</p></li>
<li><p><strong>data_z</strong> (<em>ndarray</em>) -- input data for z</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>Returns a tuple of 2 floats-- test statistic and the corresponding pvalue</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>tuple of floats</p>
</dd>
</dl>
</dd></dl>

</section>
</dd></dl>

</section>
</section>


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